The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
May. 27, 2025

Filed:

Nov. 13, 2020
Applicant:

Zhejiang University, Hangzhou, CN;

Inventors:

Zaixing He, Hangzhou, CN;

Yue Chao, Hangzhou, CN;

Dongsheng Yang, Hangzhou, CN;

Zhile Yang, Hangzhou, CN;

Assignee:

ZHEJIANG UNIVERSITY, Hangzhou, CN;

Attorneys:
Primary Examiner:
Int. Cl.
CPC ...
G05B 19/418 (2006.01); G05B 13/02 (2006.01); G06F 18/21 (2023.01); G06F 18/214 (2023.01);
U.S. Cl.
CPC ...
G05B 19/41865 (2013.01); G05B 13/027 (2013.01); G06F 18/2155 (2023.01); G06F 18/217 (2023.01);
Abstract

An adaptive-learning intelligent scheduling unified computing frame and system for industrial personalized customized production that are based on a deep neural network and reinforcement learning. An optimization algorithm is selected by automatic decision-making for a global customized production task with an industrial big data module at the bottom as an information basis, and a global optimal static scheduling plane is generated; a current dynamic event is monitored in real time; if not monitored, the global optimal static plan is executed sequentially; when a dynamic event impact requiring dynamic scheduling optimization is monitored, information of the current dynamic event is interpreted and classified, and corresponding optimization algorithms are automatically selected for dynamic scheduling optimization; and a dynamic scheduling scheme is evaluated by a subsequent module, an optimization scheme is regenerated or a most suitable optimization algorithm is automatically decided, and an equipment deployment sequence is generated for an automatic deployment.


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